Abstract
Text-to-Video Retrieval (TVR) aims to match videos with corresponding textual queries, yet the continual influx of new video content poses a significant challenge for maintaining system performance over time. In this work, we introduce the first benchmark for Continual Text-to-Video Retrieval (CTVR) to overcome these limitations. Our analysis reveals that current TVR methods based on pre-trained models struggle to retain plasticity when adapting to new tasks, while existing continual learning approaches experience catastrophic forgetting, resulting in semantic misalignment between historical queries and stored video features. To address these challenges, we propose StableFusion, a novel CTVR framework comprising two main components: the Frame Fusion Adapter (FFA), which captures temporal dynamics in video content while preserving model flexibility, and the Task-Aware Mixture-of-Experts (TAME), which maintains consistent semantic alignment between queries across tasks and the stored video features. Comprehensive evaluations on two benchmark datasets under various task settings demonstrate that StableFusion outperforms existing continual learning and TVR methods, achieving superior retrieval performance with minimal degradation on earlier tasks in the context of continuous video streams. Our code is available at: this https URL
Abstract (translated)
文本到视频检索（TVR）旨在将视频与相应的文字查询匹配，然而，新视频内容的不断涌入给保持系统的长期性能带来了巨大挑战。在这项工作中，我们介绍了第一个用于持续文本到视频检索（CTVR）的基准测试，以克服这些限制。我们的分析表明，基于预训练模型的当前TVR方法在适应新任务时难以保留可塑性，而现有的连续学习方法则会经历灾难性的遗忘现象，导致历史查询与存储视频特征之间的语义不一致。 为了解决这些挑战，我们提出了StableFusion，这是一个新的CTVR框架，包括两个主要组件：帧融合适配器（FFA），它在捕获视频内容中的时间动态的同时保持模型的灵活性；以及任务感知专家混合体(TAME)，它能够在不同的任务之间维持查询与存储视频特征之间的语义一致性。 我们在两个基准数据集上进行了广泛的评估，涵盖各种任务设置，结果表明StableFusion超越了现有的连续学习和TVR方法，在持续视频流中实现了更好的检索性能，并且对早期任务的退化最小。我们的代码可在以下网址获取：[提供链接]（请使用正确的URL替换占位符）。
URL
https://arxiv.org/abs/2503.10111
